SingularityNET AGIX: 在 YouTube 上直播
SingularityNET 将于 5 月 7 日下午 5 点（UTC）在 YouTube 上举办两场迷你 AMA 系列活动。该系列活动讨论了为 OpenCog Hyperon 开发统一体验式学习组件的最新进展，OpenCog Hyperon 是其用于人类及更高级别的通用人工智能 (AGI) 的框架。
第一场会议将介绍非公理推理系统 (NARS) 在 OpenCog Hyperon 的 MeTTa 语言认知计算中的实现，以及自主智能强化解释符号 (AIRIS) 基于因果关系的学习 AI 到 Hyperon 的集成。第二场会议将重点介绍使用 Rational OpenCog 受控代理 (ROCCA) 在 Hyperon 中重建体验式学习，以及将 ROCCA 所需的基本组件从 OpenCog 经典版移植到 Hyperon，包括正向和反向链接、概率逻辑网络 (PLN) 和模式挖掘。
活动: 2024年5月7日 17:00 UTC
什么是 AMA？
AMA，全称即Ask Me Anything。在区块链社团上很多人用AMA表示可以问我任何问题的会议。
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Join us this Tuesday, May 7th, 2024, at 5 pm UTC for the first session of a special two-part SingularityNET's Technical Tuesdays mini-series dedicated to the latest advancements in the development of a unified experiential learning component for OpenCog Hyperon, our framework for #AGI at the human level and beyond.
Session 1
- The implementation of NARS (Non-Axiomatic Reasoning System) in OpenCog Hyperon’s MeTTa language cognitive computations;
- Integrating the AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism) causality-based learning AI into Hyperon.
Session 2
- Recreating experiential learning in Hyperon using ROCCA (Rational OpenCog Controlled Agent);
- Porting fundamental components ROCCA requires from OpenCog classic to Hyperon, including forward and backward chaining, PLN (Probabilistic Logic Networks), and pattern mining.
These advancements are part of our ongoing initiative to consolidate the strengths of several systems —ROCCA, NARS, OpenPsi, and AIRIS— to create a unified experiential learning component for Hyperon. This approach will allow AI models to:
- Develop a goal-independent understanding of their environment through causal knowledge gained from planned and spontaneous interactions;
- Explore their environment with increased efficiency using a curiosity model that prioritizes situations with high uncertainty, challenging their existing causal knowledge.
Our preliminary findings indicate that this approach surpasses common Reinforcement Learning techniques in terms of data efficiency by orders of magnitude.
To learn more, set your reminder for the livestream now on your preferred platform:
- YouTube: https://t.co/66Xm1SpDC8
- LinkedIn: https://t.co/JEwoyT7Z9R
- X: SingularityNET
Session 1
- The implementation of NARS (Non-Axiomatic Reasoning System) in OpenCog Hyperon’s MeTTa language cognitive computations;
- Integrating the AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism) causality-based learning AI into Hyperon.
Session 2
- Recreating experiential learning in Hyperon using ROCCA (Rational OpenCog Controlled Agent);
- Porting fundamental components ROCCA requires from OpenCog classic to Hyperon, including forward and backward chaining, PLN (Probabilistic Logic Networks), and pattern mining.
These advancements are part of our ongoing initiative to consolidate the strengths of several systems —ROCCA, NARS, OpenPsi, and AIRIS— to create a unified experiential learning component for Hyperon. This approach will allow AI models to:
- Develop a goal-independent understanding of their environment through causal knowledge gained from planned and spontaneous interactions;
- Explore their environment with increased efficiency using a curiosity model that prioritizes situations with high uncertainty, challenging their existing causal knowledge.
Our preliminary findings indicate that this approach surpasses common Reinforcement Learning techniques in terms of data efficiency by orders of magnitude.
To learn more, set your reminder for the livestream now on your preferred platform:
- YouTube: https://t.co/66Xm1SpDC8
- LinkedIn: https://t.co/JEwoyT7Z9R
- X: SingularityNET
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